Background: The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for testing differential expression of genes utilize hierarchical Bayesian models to "pool" information from multiple genes. We have developed a statistical testing procedure that further improves upon current methods by incorporating the well-documented relationship between the absolute gene expression level and the variance of gene expression measurements into the general empirical Bayes framework.Results: We present a novel Bayesian moderated-T, which we show to perform favorably in simulations, with two real, dual-channel microarray experiments and in two controlled single-channel experiments. In simulations, the new method achieved greater power while correctly estimating the true proportion of false positives, and in the analysis of two publicly-available "spike-in" experiments, the new method performed favorably compared to all tested alternatives. We also applied our method to two experimental datasets and discuss the additional biological insights as revealed by our method in contrast to the others. The R-source code for implementing our algorithm is freely available athttp://eh3.uc.edu/ibmt.Conclusion: We use a Bayesian hierarchical normal model to define a novel Intensity-Based Moderated T-statistic (IBMT). The method is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters. IBMT has the strength of balancing two important factors in the analysis of microarray data: the degree of independence of variances relative to the degree of identity (i.e.t-tests vs. equal variance assumption), and the relationship between variance and signal intensity. When this variance-intensity relationship is weak or does not exist, IBMT reduces to a previously described moderated t-statistic. Furthermore, our method may be directly applied to any array platform and experimental design. Together, these properties show IBMT to be a valuable option in the analysis of virtually any microarray experiment.

Purpose: Patients with resected, local–regionally advanced, head and neck squamous cell carcinoma (HNSCC) have a one-year disease-free survival (DFS) rate of 65%–69% despite adjuvant (chemo)radiotherapy. Neoadjuvant PD-1 immune-checkpoint blockade (ICB) has demonstrated clinical activity, but biomarkers of response and effect on survival remain unclear. Patients and Methods: Eligible patients had resectable squamous cell carcinoma of the oral cavity, larynx, hypopharynx, or oropharynx (p16-negative) and clinical stage T3-T4 and/or two or more nodal metastases or clinical extracapsular nodal extension (ENE). Patients received neoadjuvant pembrolizumab 200 mg 1–3 weeks prior to surgery, were stratified by absence (intermediate-risk) or presence (high-risk) of positive margins and/or ENE, and received adjuvant radiotherapy (60–66 Gy) and concurrent pembrolizumab (every 3 weeks × 6 doses). Patients with high-risk HNSCC also received weekly, concurrent cisplatin (40 mg/m2). Primary outcome was one-year DFS. Secondary endpoints were one-year overall survival (OS) and pathologic response (PR). Safety was evaluated with CTCAE v5.0. Results: From February 2016 to October 2020, 92 patients enrolled. The median age was 59 years (range, 27–80), 30% were female, 86% had stage T3–T4, and 69% had ≥N2. At a median follow-up of 28 months, one-year DFS was 97% (95% CI, 71%–90%) in the intermediate-risk group and 66% (95% CI, 55%–84%) in the high-risk group. Patients with a PR had significantly improved one-year DFS relative to patients without response (93% vs. 72%, hazard ratio 0.29; 95% CI, 11%–77%). No new safety signals were identified. Conclusions: Neoadjuvant and adjuvant pembrolizumab increased one-year DFS rate in intermediate-risk, but not high-risk, HNSCC relative to historical control. PR to neoadjuvant ICB is a promising surrogate for DFS.

Motivation: Identifying patterns of co-expression in microarray data by cluster analysis has been a productive approach to uncovering molecular mechanisms underlying biological processes under investigation. Using experimental replicates can generally improve the precision of the cluster analysis by reducing the experimental variability of measurements. In such situations, Bayesian mixtures allow for an efficient use of information by precisely modeling between-replicates variability. Results: We developed different variants of Bayesian mixture based clustering procedures for clustering gene expression data with experimental replicates. In this approach, the statistical distribution of microarray data is described by a Bayesian mixture model. Clusters of co-expressed genes are created from the posterior distribution of clusterings, which is estimated by a Gibbs sampler. We define infinite and finite Bayesian mixture models with different between-replicates variance structures and investigate their utility by analyzing synthetic and the real-world datasets. Results of our analyses demonstrate that (1) improvements in precision achieved by performing only two experimental replicates can be dramatic when the between-replicates variability is high, (2) precise modeling of intra-gene variability is important for accurate identification of co-expressed genes and (3) the infinite mixture model with the ‘elliptical’ between-replicates variance structure performed overall better than any other method tested. We also introduce a heuristic modification to the Gibbs sampler based on the ‘reverse annealing’ principle. This modification effectively overcomes the tendency of the Gibbs sampler to converge to different modes of the posterior distribution when started from different initial positions. Finally, we demonstrate that the Bayesian infinite mixture model with ‘elliptical’ variance structure is capable of identifying the underlying structure of the data without knowing the ‘correct’ number of clusters. Availability: The MS Windows™ based program named Gaussian Infinite Mixture Modeling (GIMM) implementing the Gibbs sampler and corresponding C++ code are available at http://homepages.uc.edu/~medvedm/GIMM.htm Supplemental information:http://expression.microslu.washington.edu/expression/kayee/medvedovic2003/medvedovic_bioinf2003.html

The vast amount of RNA-seq data deposited in Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) is still a grossly underutilized resource for biomedical research. To remove technical roadblocks for reusing these data, we have developed a web-application GREIN (GEO RNA-seq Experiments Interactive Navigator) which provides user-friendly interfaces to manipulate and analyze GEO RNA-seq data. GREIN is powered by the back-end computational pipeline for uniform processing of RNA-seq data and the large number (>6,500) of already processed datasets. The front-end user interfaces provide a wealth of user-analytics options including sub-setting and downloading processed data, interactive visualization, statistical power analyses, construction of differential gene expression signatures and their comprehensive functional characterization, and connectivity analysis with LINCS L1000 data. The combination of the massive amount of back-end data and front-end analytics options driven by user-friendly interfaces makes GREIN a unique open-source resource for re-using GEO RNA-seq data. GREIN is accessible at:https://shiny.ilincs.org/grein, the source code at:https://github.com/uc-bd2k/grein, and the Docker container at:https://hub.docker.com/r/ucbd2k/grein.

Motivation: The elucidation of biological pathways enriched with differentially expressed genes has become an integral part of the analysis and interpretation of microarray data. Several statistical methods are commonly used in this context, but the question of the optimal approach has still not been resolved. Results: We present a logistic regression-based method (LRpath) for identifying predefined sets of biologically related genes enriched with (or depleted of) differentially expressed transcripts in microarray experiments. We functionally relate the odds of gene set membership with the significance of differential expression, and calculate adjusted P-values as a measure of statistical significance. The new approach is compared with Fisher's exact test and other relevant methods in a simulation study and in the analysis of two breast cancer datasets. Overall results were concordant between the simulation study and the experimental data analysis, and provide useful information to investigators seeking to choose the appropriate method. LRpath displayed robust behavior and improved statistical power compared with tested alternatives. It is applicable in experiments involving two or more sample types, and accepts significance statistics of the investigator's choice as input. Availability: An R function implementing LRpath can be downloaded from http://eh3.uc.edu/lrpath. Contact: mario.medvedovic@uc.edu Supplementary information: Supplementary data are available at Bioinformatics online and at http://eh3.uc.edu/lrpath.

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program with the goal of generating a large-scale and comprehensive catalogue of perturbation-response signatures by utilizing a diverse collection of perturbations across many model systems and assay types. The LINCS Data Portal (LDP) has been the primary access point for the compendium of LINCS data and has been widely utilized. Here, we report the first major update of LDP (http://lincsportal.ccs.miami.edu/signatures) with substantial changes in the data architecture and APIs, a completely redesigned user interface, and enhanced curated metadata annotations to support more advanced, intuitive and deeper querying, exploration and analysis capabilities. The cornerstone of this update has been the decision to reprocess all high-level LINCS datasets and make them accessible at the data point level enabling users to directly access and download any subset of signatures across the entire library independent from the originating source, project or assay. Access to the individual signatures also enables the newly implemented signature search functionality, which utilizes the iLINCS platform to identify conditions that mimic or reverse gene set queries. A newly designed query interface enables global metadata search with autosuggest across all annotations associated with perturbations, model systems, and signatures.